Detection of weak chaos in infant respiration

This paper concerns the application of newly developed methods for decomposition of an infant respiratory signal into locally stable nonsinusoidal periodic components. Each estimated component has dynamical variation in its three periodicity attributes, i.e., periodicity, scaling factors, and the waveform or pattern associated with the successive segments. Earlier, it has been reported with the application of conventional surrogate analysis and with the cylindrical basis function modeling that the underlying system is distinctly different from linearly filtered Gaussian process, and most probably the human respiratory system behaves as a nonlinear periodic oscillator with two or three degrees of freedom being driven by a high-dimensional noise source. Here, the surrogate analysis is extended and four new types of nonlinear surrogates have been proposed, which are produced by randomizing one or multiple periodicity attributes while preserving certain individual relationships. In this way, a new type of dissection of dynamics is possible, which can lead to a proper understanding of coupling between different controlling parameters.